Do machine learning methods lead to similar individualized treatment rules? A comparison study on real data.
Florie Bouvier1, Etienne Peyrot1, Alan Balendran1
1Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), Université Paris Cité and Université Sorbonne Paris Nord, Paris, France.
Statistics in Medicine
|March 13, 2024
Summary
Different machine learning methods for individualized treatment rules (ITRs) show significant disagreement in patient recommendations. This variability raises concerns about the practical application and interchangeability of these personalized medicine approaches.
Area of Science:
- * Biostatistics
- * Machine Learning
- * Personalized Medicine
Background:
- * Personalized medicine aims to tailor treatments using individualized treatment rules (ITRs).
- * Numerous machine learning methods exist for ITR development, but their comparative performance and agreement are not well-understood.
Purpose of the Study:
- * To compare the performance and agreement of 22 common machine learning methods for generating ITRs.
- * To assess the extent to which different methods yield similar treatment recommendations for patients.
Main Methods:
- * Evaluation of 22 machine learning approaches across two randomized controlled trials.
- * Categorization of methods into those predicting individualized treatment effects versus those directly estimating ITRs.
- * Assessment of ITR performance using various metrics and calculation of pairwise agreement between ITRs.
Main Results:
- * Significant disagreements were observed among ITRs generated by different methods regarding patient selection for treatment.
- * Methods with similar underlying approaches showed better concordance.
- * ITRs evaluated on validation samples exhibited limited performance, indicating potential overfitting, especially in non-parametric methods.
Conclusions:
- * The choice of machine learning method substantially impacts patient-treatment recommendations.
- * Different methods for ITRs are not interchangeable and produce dissimilar outcomes.
- * The variability and potential for optimism in performance metrics raise concerns about the clinical utility of current ITR methods.
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